Earth observation-based long-term global vegetation index products are used by scientists from a wide range of disciplines concerned with global change. Inter-comparison studies are commonly performed to keep the user community informed on the consistency and accuracy of such records as they evolve. In this study, we compared two new records: (1) Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index version 3 (NDVI3g) and (2) Vegetation Index and Phenology Lab (VIP) version 3 NDVI (NDVI3v) and enhanced vegetation index 2 (EVI3v). We evaluated the two records via three experiments that addressed the primary use of such records in global change research: (1) leaf area index (LAI), (2) vegetation climatology, and (3) trend analysis of the magnitude and timing of vegetation productivity. Unlike previous global studies, a unique Landsat 30¯m spatial resolution and in situ LAI database for major crop types on five continents was used to evaluate the performance of not only NDVI3g and NDVI3v but also EVI3v. The performance of NDVI3v and EVI3v was worse than NDVI3g using the in situ data, which was attributed to the fusion of GIMMS and MODIS data in the VIP record. EVI3v has the potential to contribute biophysical information beyond NDVI3g and NDVI3v to global change studies, but we caution its use due to the poor performance of EVI3v in this study. Overall, the records were most consistent at northern latitudes during the primary growing season and southern latitudes and the tropics throughout much of the year, while the records were less consistent at northern latitudes during green-up and senescence, and in the great deserts of the world throughout much of the year. These patterns led to general agreement (disagreement) between trends in the magnitude (timing) of NDVI over the study period. Bias in inter-calibration of the VIP record at northernmost latitudes was suspected to contribute most to these discrepancies. © 2016 Author(s).
Tag: climatology
Proximal remote sensing and gross primary productivity in a temperate salt marsh
Salt marshes are highly productive ecosystems relevant for Blue Carbon assessments, but information for estimating gross primary productivity (GPP) from proximal remote sensing (PRS) is limited. Temperate salt marshes have seasonal canopy structure and metabolism changes, defining different canopy phenological phases, GPP rates, and spectral reflectance. We combined multi-annual PRS data (i.e., PhenoCam, discrete hyperspectral measurements, and automated spectral reflectance sensors) with GPP derived from eddy covariance. We tested the performance of empirical models to predict GPP from 12 common vegetation indices (VIs; e.g., NDVI, EVI, PSRI, GCC), Sun-Induced Fluorescence (SIF), and reflectance from different areas of the electromagnetic spectrum (i.e., VIS-IR, RedEdge, IR, and SIF) across the annual cycle and canopy phenological phases (i.e., Greenup, Maturity, Senescence, and Dormancy). Plant Senescence Reflectance Index (PSRI) from hyperspectral data and the Greenness Index (GCC) from PhenoCam, showed the strongest relationship with daily GPP across the annual cycle and within phenological phases (r2=0.30–0.92). Information from the visible-infrared electromagnetic region (VIS-IR) coupled with a partial least square approach (PLSR) showed the highest data-model agreement with GPP, mainly because of its relevance to respond to physiological and structural changes in the canopy, compared with indices (e.g., GCC) that particularly react to changes in the greenness of the canopy. The most relevant electromagnetic regions to model GPP were ∼550 nm and ∼710 nm. Canopy phenological phases impose challenges for modeling GPP with VIs and the PLSR approach, particularly during Maturity, Senescence, and Dormancy. As more eddy covariance sites are established in salt marshes, the application of PRS can be widely tested. Our results highlight the potential to use canopy reflectance from the visible spectrum region for modeling annual GPP in salt marshes as an example of advances within the AmeriFlux network.
Functional biogeography of Neotropical moist forests: Trait–climate relationships and assembly patterns of tree communities
Aim: Here we examine the functional profile of regional tree species pools across the latitudinal distribution of Neotropical moist forests, and test trait–climate relationships among local communities. We expected opportunistic strategies (acquisitive traits, small seeds) to be overrepresented in species pools further from the equator, but also in terms of abundance in local communities in currently wetter, warmer and more seasonal climates. Location: Neotropics. Time period: Recent. Major taxa studied: Trees. Methods: We obtained abundance data from 471 plots across nine Neotropical regions, including c. 100,000 trees of 3,417 species, in addition to six functional traits. We compared occurrence-based trait distributions among regional species pools, and evaluated single trait–climate relationships across local communities using community abundance-weighted means (CWMs). Multivariate trait–climate relationships were assessed by a double-constrained correspondence analysis that tests both how CWMs relate to climate and how species distributions, parameterized by niche centroids in climate space, relate to their traits. Results: Regional species pools were undistinguished in functional terms, but opportunistic strategies dominated local communities further from the equator, particularly in the Northern Hemisphere. Climate explained up to 57% of the variation in CWM traits, with increasing prevalence of lower-statured, light-wooded and softer-leaved species bearing smaller seeds in more seasonal, wetter and warmer climates. Species distributions were significantly but weakly related to functional traits. Main conclusions: Neotropical moist forest regions share similar sets of functional strategies, from which local assembly processes, driven by current climatic conditions, select for species with different functional strategies. We can thus expect functional responses to climate change driven by changes in relative abundances of species already present regionally. Particularly, equatorial forests holding the most conservative traits and large seeds are likely to experience the most severe changes if climate change triggers the proliferation of opportunistic tree species. © 2021 John Wiley & Sons Ltd
Climate change in Central Asia: Sino-German cooperative research findings
Central Asia (CA) occupies the hinterland of the Eurasian continent, containing the countries of Uzbekistan, Kyrgyzstan, Turkmenistan, Tajikistan, and Kazakhstan. Being isolated by the Pamir Mountains in Tajikistan, the Tibetan Plateau and the Tian Shan Mountains on the border between China and Kyrgyzstan, the moist air from the Pacific and Indian Oceans has difficulty reaching this region. More than 93% of the total area is dry land (including arid, semiarid, and subhumid areas, as defined by the United Nations Convention to Combat Desertification), and the ecosystems of this region are very sensitive and susceptible to climate change. In order to better perform sustainable management in CA, which is an important pivot area along the “Silk Road Economic Belt”, it is crucial to understand regional climate change and its interaction with water resources, land cover, and ecosystems.